Interpretable Performance Models for Energetic Materials using Parsimonious Neural Networks.

Predictive models for the performance of explosives and propellants are important for their design, optimization, and safety. Thermochemical codes can predict some of these properties from fundamental quantities such as density and formation energies that can be obtained from first principles. Models that are simpler to evaluate are desirable for efficient, rapid screening of material screening.

Prediction of Energetic Material Properties from Electronic Structure Using 3D Convolutional Neural Networks.

We develop a convolutional neural network capable of directly parsing the 3D electronic structure of a molecule described by spatial point data for charge density and electrostatic potential represented as a 4D tensor. This method effectively bypasses the need to construct complex representations, or descriptors, of a molecule. This is beneficial because the accuracy of a machine learned model depends on the input representation.

Dynamic imaging of the temperature field within an energetic composite using phosphor thermography.

An improved understanding of energy localization ("hot spots") is needed to improve the safety and performance of explosives. We propose a technique to visualize and quantify the properties of a dynamic hot spot from within an energetic composite subjected to ultrasonic mechanical excitation. The composite is composed of an optically transparent binder and a countable number of octahydro 1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX) crystals.